Why shoppers use their smartphone for an in-store purchase?

Gwarlann de Kerviler*

Assistant Professor of Marketing

IESEG School of Management

Nathalie T.M. Demoulin

Associate Professor of Marketing

IESEG School of Management

Pietro Zidda

Professor of Marketing

University of Namur

Center for Research on Consumption & Leisure (CeRCLe)

* IESEG School of Management, Socle de la Grande Arche, 1 Parvis de La Défense, 92044

Paris La Défense cedex, France, g.dekerviler@ieseg.fr, +33 (0) 1 55 91 10 10.

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Pourquoi les acheteurs utilisent leur smartphone pour un achat en magasin?

Résumé

Le nombre croissant d’utilisateurs d’un smartphone pour faire du shopping pousse les

distributeurs à évaluer son impact sur le comportement de leurs clients. Cet article étudie les

facteurs d’adoption d’un smartphone comme assistant tout au long du processus d’achat en

magasin. En utilisant diverses théories relatives au comportement du consommateur, nous

identifions trois étapes clés du processus. Sur base de données d’enquêtes, nos analyses

montrent d’une part que les déterminants de l’attitude envers l’utilisation du smartphone

comme assistant varient d’une étape à l’autre et d’autre part que l’effet des influences

sociales, de l’expérience et des conditions de facilitation jouent un rôle différent selon l’étape

du processus.

Mots-clés : processus décisionnel d’achat, marketing mobile, assistant de shopping, adoption

Why shoppers use their smartphone for an in-store purchase?

Abstract

With the increasing number of shoppers using smartphones while shopping, retailers need to

understand how it impacts their customers’ shopping process. This article investigates

motivations to and barriers of smartphone usage for shopping activities. Building on various

consumer behavior theories, we identify three stages on the path to an in-store purchase: pre-

shopping, pre-purchase and purchase. Based on survey data, our analyses highlight that in a

retail setting the drivers of the attitude towards smartphone usage vary across shopping stages

and that social influences, shopper experience and facilitating conditions play differing roles

in each stage.

Key-words: shopping decision process, mobile marketing, shopping assistant, adoption

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Why shoppers use their mobile phone for an in-store purchase?

1. Introduction

Consumers go through multiple channels when shopping (Powers et al., 2012; Vanheems,

2013) and 60% of smartphone users now use their mobiles as a shopping assistant to prepare

and complete purchases in the store (Miller and Washington, 2013). The present study aims at

gaining a better understanding of the attitude towards adoption and usage of smartphones as a

shopping assistant. It is the first to analyze specifically how smartphones could be used for in-

store purchases and it thus provides useful information for retailers to design relevant mobile

apps or mobile websites that are likely to enhance their customers’ shopping experience. This

research also complements past studies, which have solely considered the smartphone either

as a communication tool for SMS advertising (e.g., Yang, Kim and Yoo, 2013) and SMS

promotions (Khajehzadeh, Oppewal and Tojib, 2014), as an informational and decision tool in

stores (Van der Heijden, 2006) or as a substitute channel to e-commerce via PC, to brick-and-

mortar stores or to catalogues (Turban et al., 2004).

For completing an in-store purchase, shoppers go through various activities along the

stages of the shopping process, such as creating a shopping list, querying retailers, searching

for the right products, comparing items and purchasing them (Shankar et al., 2010). Different

benefit-driven motivations influence the choice of a particular channel for each activity or

stage (Balasubramanian, Raghunathan and Mahajan, 2005; Frambach, Roest and Krishnan,

2007). Shoppers search for relevant channel attributes at each stage (Gensler, Verhoef and

Böhm, 2012). In the search stage, consumers gather information, while minimizing search

efforts and costs. Then they evaluate alternatives based on assortment, quality and price. At

the purchase stage, shoppers strive to buy the selected product at the lowest price available,

while avoiding privacy and payment risks. Our contribution is twofold: (1) This paper aims to

compare the specific benefit-driven motivations behind smartphone usage at each stage on the

path to an in-store purchase. It thus extends past research which has limited mobile usage to

in-store information search (Van der Heijde, 2005) or to product purchases (Kumar and

Mukherjee, 2013); (2) For the stages along the path of an in-store purchase, we differentiate

between the retailer choice and the product choice as suggested by Levy and Weitz (2012). In

addition to a “pre-purchase” stage inside the store during which shoppers make their product

choice and a “purchase” stage, we indeed consider a “pre-shopping” stage outside the store,

mainly characterized by the shopper’s choice of store. The pre-shopping stage, ignored in

previous studies on channel preference, is based on Bell, Corsten and Knox’s (2011) work,

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which identifies a preliminary stage outside the store. The store choice stage has often been

considered to be specific in the decision making process (Bell, Ho and Tang, 1998) and to

affect not only where shoppers buy but also what and how much they purchase when in the

store (Briesch, Chintagunta and Fox, 2009). The pre-purchase stage is similar to the stage

analyzed by Van der Heijde (2005) who identified “forming a consideration set of products in

store” as a central step in the purchase decision making process. Understanding shoppers’

perceived benefits and risks associated with smartphone usage at various stages of the

shopping process is crucial for managers who want to enrich their customers’ experience

through mobile marketing.

2. Theoretical background and hypotheses

To determine antecedents of the attitude towards smartphone usage for an in-store purchase,

we based our conceptual model on the theory of planned behavior (TPB) (Taylor and Todd,

1995), which has been proven to be successful in predicting adoption of innovation (Morris,

Venkatesh and Ackerman, 2005). We enriched the model with the risk/reward perspective

regarding technological innovation (Wells et al., 2010) and with the task-technology fit

(Goodhue and Thompson, 1995) to gain a more thorough analysis of motivations and barriers

influencing smartphone usage.

The theory of planned behavior (TPB) is rooted in the technology acceptance literature,

which studies individual reactions to a new technology. The theory of reasoned action (TRA)

has often been applied to analyze acceptance of mobile advertising (Bauer et al., 2005; Zhang

and Mao, 2008) and suggests that intentions are influenced by attitude towards the behavior

and by others’ opinions about that behavior (Fishbein and Ajzen, 1975). TPB broadens TRA

to account for conditions where individuals do not have complete control over their behavior

(Taylor and Todd, 1995).

1.1. Attitude toward smartphone usage and its antecedents

To determine the antecedents of the attitude towards smartphone usage as a shopping

assistant, we rely on Wells et al. (2010) risk/rewards approach, as well as on the task-

technology fit (Goodhue and Thompson, 1995). We identify four smartphone benefits, i.e.

access to information, economic, convenience and social, and two barriers, i.e. privacy and

financial risks. Information benefits are associated with smartphone rapid and easy access to a

large quantity of details regarding stores and their merchandise (Varshney, Vetter and

Kalakota, 2000) without spatial or temporal restrictions. Economic benefits denote

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perceptions of good value for money (Sheth et al., 1991) because shoppers receive

promotional offers and compare prices on their mobile. Helping a shopper to conduct

transactions more conveniently has a positive effect on channel choice (Gensler, Verhoef and

Böhm, 2012) and smartphone usage can enhance shopping convenience –or minimize

required time and effort in one’s shopping– through mobile payment, retrieval of stored

information and access to mobile loyalty cards. Sweeney and Soutar (2001) define social

benefits as the utility derived from enhancing social self-concept. Consumers may express

their social identity when shopping. As such, adoption of mobile services are influenced by

their perceived projected image on others (Laukkanen et al., 2007). Perceived risk

corresponds to a combination of uncertainty plus seriousness of the outcome involved (Bauer,

1967, p. 25) and has a negative influence on choice of a channel (e.g., Gensler, Verhoef and

Böhm, 2012). Transposed to mobile usage and payments, the privacy and financial risks are

linked to potential monetary and psychological losses due respectively to a lower control over

personal information (Featherman and Pavlou, 2003; Hérault and Belvaux, 2014) and to

transaction errors or fraudulent uses of banking account information (Lee, 2008).

1.2. Smartphone usage intention and its determinants

In addition to the attitude towards usage, past research has highlighted the role of social

influence (according to Azjen (1987), peer influence to which a person’s behavior is exposed

to) and experience on adoption (e.g., Venkatesh, Thong and Xu, 2012). Former studies also

demonstrate that, because it creates a cognitive lock-in, experience with a specific channel

influences consumers’ channel choice (Frambach, Roest and Krishnan, 2007; Gensler,

Verhoef and Böhm, 2012). Facilitating conditions are consumer perceptions of resources,

constraints and support available to perform a behavior (Taylor and Todd, 1995). A consumer

who has access to a favorable set of facilitating conditions is more likely to use a technology

(Venkatesh, Thong and Xu, 2012) and smartphone usage intentions should be higher when all

necessary resources (e.g., time, money, Wi-Fi or mobile internet connections) are available.

1.3. Stages in the shopping process

The shopping process consists of distinct stages and because goals differ at each stage (e.g.,

Lee and Ariely, 2006), channel preferences are likely to differ across the stages of the

shopping cycle (Frambach, Roest and Krishnan, 2007). According to the task-technology fit

approach (Goodhue and Thompson, 1995), a technology should provide features that fit the

requirements of a task. Thus, smartphone usage for an in-store purchase should be linked to

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specific requirements at each stage of the shopping process.

As detailed in the introduction, the shopping stages for an in-store purchase used in our

study were carefully chosen from a review of the buying process described by Levy and

Weitz (2012), Bell, Corsten and Knox (2011) and Van der Heidjen (2005). We distinguish

between a pre-shopping stage (S1), outside the store, during which shoppers set their

shopping goals and compare different retailers (Bell, Corsten and Knox, 2011), a pre-

purchase stage (S2) consisting of selecting a product in the store (Van der Heidjen, 2005), and

a purchase stage (S3) consisting in buying a selected product in the store.

At S1, when the shopper is outside of the store, the task is to plan efficiently one’s

shopping by narrowing down the consideration set (Balasubramanian, Raghunathan and

Mahajan, 2005). Shoppers compare stores according to a list of criteria, such as prices,

assortment, service or convenience (Bell, Corsten and Knox, 2011) before selecting a

particular retailer (Levy and Weitz, 2012). Mobile technology provides quick access to

information about stores, such as locations, opening hours, parking facilities and merchandise.

Smartphones can also be used to compare store prices with comparison apps and to find

retailers’ mobile coupons, thereby influencing the store choice. Information and economic

benefits increase efficiency in one’s shopping (Mourali, Laroche and Pons, 2005) and should

positively influence attitude. Moreover, experienced users who know how to take advantage

of smartphone functions (e.g., mapping one’s environment, downloading store information)

and who have appropriate resources (e.g., easy access to internet through their mobiles) will

be more inclined to use their smartphones to plan their shopping.

The key task involved in S2, when the shopper is inside the store, consists in selecting

merchandise in the store (Levy and Weitz, 2012; Van der Heijde, 2005). Smartphone

functions can help scan QR codes for product information, access mobile applications, share

information with others about an item or display oneself trying the product. Smartphone usage

inside the store can project an image of a smart shopper using a mobile assistant. However,

using mobile functions may also give retailers access to personal information (e.g., user

profiles, location history, in-store behavior, online activities) and enable them to track

customers in the store with geolocation (Hérault and Belvaux, 2014) or bluetooth

technologies (e.g., iBeacon). Provided that personal privacy is protected, the ability to access

product information and to enhance one’s perceived image is particularly important at S2.

Moreover, access to functionalities such as scanning QR codes or sending pictures requires

some experience which should positively influence intention. Frambach et al. (2007) showed

that as consumers progress in the decision making process, social influences become more

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influential. Finally, facilitating conditions influence usage, as access to the internet through

the retailer’s Wi-Fi or mobile internet is needed.

In S3, the task is to finalize the purchase in the store in a convenient way (Frambach et al.,

2007), as well as to buy the merchandise at the lowest price (e.g., Balasubramanian et al.,

2005). Smartphones can be used as a digital wallet, transferring funds electronically from

(almost) anywhere, anytime, and to pay and redeem coupons at the time of purchase. Such

usage may be perceived as innovative and project an image of a technology savvy person.

However, customer confidence is still low when it comes to mobile transactions (Jarvenpaa et

al., 2000). Hence, we expect perceived convenience, economic and social benefits to

positively influence attitude, and perceived risks to negatively influence attitude at this stage.

Consumers feel a strong need to be reassured at the time of payment (Frambach et al., 2007)

leading to social norms’ influence on usage intentions at S3. Even experienced users with

access to various functionalities (e.g., access to mobile wallets) may not be more inclined than

other customers to use their smartphones. Therefore experience and facilitating conditions

should have no impact on usage intention and usage frequency at this stage.

To sum up, we suggest the following conceptual model and hypotheses:

Figure 1. Conceptual model

H1: Stages of the shopping process moderate the effect of perceived benefits and perceived

risks on the attitude towards smartphone usage, such that

H1a: Economic benefits influence attitude more during S1 and S3 in comparison to S2

H1b: Convenience benefits influence attitude more during S3 in comparison to S1 and S2

H1c: Information benefits influence attitude more during S1 and S2 in comparison to S3

H1d: Social benefits influence attitude more during S2 and to S3 in comparison to S1

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H1e: Risks influence attitude more during S2 and S3 in comparison to S1

H2: Stages of the buying process moderate the effect of social influence and experience on

smartphone usage intention, such that:

H2a: Social influence influences usage intention more during S2 and S3 in comparison to S1.

H2b: Experience influences usage intention more during S1 and S2 in comparison to S3.

H3: The effect of facilitating conditions on usage frequency is stronger at S1 and S2 than at

S3.

3. Data collection

In order to collect our data, we used a scenario-based survey approach, each scenario

corresponding to a stage of a shopping process in the context of the purchase of a new

compact camera. 541 consumers with at least one online purchase past experience (48%

women and 52% men, aged 20 to 64 with an average age of 35) were surveyed by means of

an online questionnaire. We first asked our respondents to read a general introduction

clarifying the use of the smartphone for shopping purposes (see Appendix 1). We then asked

them to read the details about the three scenarios (i.e., stages) so that they understood the

whole shopping process and we elicited their intention to use a smartphone as a shopping

assistant at each stage (Venkatesh, Thong and Xu, 2012). It was then made clear that each

respondent would be placed in a single stage for the rest of the questionnaire. We then

randomly assigned respondents to one of the three stages and asked them to answer to the

questions related to that particular stage only. Respondents in S1 were placed in an out-of-

home situation with the task of selecting a retailer to visit for the purchase of a new compact

camera; those in S2 were placed in a store with the need to choose a compact camera to

purchase; and those in S3 were again placed in a store, with a payment task for the chosen

compact camera. Within each scenario, participants were first asked about their attitude

towards using a smartphone as a shopping assistant (Taylor and Todd, 1995) and about their

usage frequency (Venkatesh, Thong and Xu, 2012) at that particular stage. We then elicited,

with respect to using a smartphone as a shopping assistant at that stage, the perceived

economic benefits (Mimouni-Chaabanne and Volle, 2010), convenience benefits (Childers,

2001), informational benefits (TNS, 2013), social benefits (Sweeney and Soutar, 2001),

privacy and financial risks (Featherman and Pavlou, 2003), facilitating conditions and social

influences (Venkatesh, Thong and Xu, 2012) as well as smartphone experience (Murray and

Schlacter, 1990). We also measured a set of controlling variables using the scales available in

the literature (i.e., computer experience, purchase decision involvement, product

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involvement). All constructs but usage frequency were measured on a 7-point agreement

scale. Usage frequency was measured on a 7-point frequency scale, ranging from “never” to

“always”.

4. Results

We analyzed the data using SmartPLS version 2.0.M3 (Ringle et al., 2005) for two stages

related to the measurement model and the structural model. We have respectively 178, 179

and 184 respondents for S1, S2 and S3. First, we tested the measurement model for the entire

sample and for each stage by performing a validity and reliability analysis for each measure in

the structural model by using the appropriate tests (Fornell and Larcker, 1981). As shown in

Appendix 2, the measures are reliable and valid overall. Moreover, we tested for measurement

invariance across stages (Eberl, 2010). Our results show evidence of invariance across stages.

Finally, we performed ANOVAs to compare respondents across stages in terms of age,

computer experience, smartphone experience, decision making involvement and product

involvement. No significant differences were found. Thus, the three groups can be considered

to be similar.

In order to test our hypotheses, we performed multi-group analyses applied to PLS (Eberl,

2010). Table 1 presents the path coefficients of the model tested for each stage and for the

total sample and their statistical significance (using bootstrapping resampling techniques).

According to Chin (1998), the R-squared values for our main dependent variables can be

considered substantial for attitude (i.e., stage 1: R²= .66; stage 2: R²= .60; stage 3: R² = .56)

and usage intention (i.e., stage 1: R²= .66; stage 2: R²= .54; stage 3: R² = .69) and rather

moderate for usage frequency (i.e., stage 1: R²= .53; stage 2: R²= .51; stage 3: R² = .34). The

GoF of models for the three stages are good (ranging from .71 to .76). Looking at the Q-

square values, the model has predictive relevance (Fornell and Cha, 1994). According to

Henseler et al. (2009), the dependent variable Q-square values in each of the three models are

evaluated as large, i.e. between .58 and .69 for attitude, between .48 and .65 for usage

intention and between .33 to .52 for usage frequency.

To verify H1, H2 and H3, we performed parameter comparisons using t tests as recommended

by Eberl (2010) in multi-group analysis. The effect of economic benefits on attitude is

positive and significant at S1 and S3. The effect at S1 and S3 is significantly higher than at S2

(S1/S2: t=2.33, p< 05; S2/S3: t=1.74, p<.05). Thus, H1a is supported. Convenience benefits

impact attitude only at S3 and the influence is marginally higher than in other stages (S1/S3:

t=1.54, p<.1; S2/S3: t=1.61, p<.1). Thus, H1b is partially supported. Information benefits

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have a significant impact on attitude at S1 and S2 and parameters significantly differ

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Pre-shopping (S1) Pre-purchase (S2) Purchase (S3) Total sample

Std

coef

t-value Std

coef

t-value Std

coef

t-value Std

coef

t-value

Economic Benefits Attitude 0,36 **

2,76 -0,01

0,10 0,21 * 2,37 0,30

*** 5,30

Convenience Benefits Attitude 0,11

0,94 0,18

1,81 0,34 ***

3,75 0,20 ***

3,47

Information Benefits Attitude 0,36 **

2,58 0,56 ***

6,00 0,09

1,11 0,23 ***

4,38

Social Benefits Attitude 0,07

1,19 0,15 *

1,98 0,22 ***

3,52 0,16

4,40

Risks Attitude 0,01

0,17 -0,20 *

2,15 -0,24 ***

4,39 -0,20 **

6,31

Attitude Usage Intention 0,55 ***

6,12 0,38 ***

5,01 0,65 ***

8,86 0,55 ***

11,34

Social Influences Usage Intention 0,10

1,31 0,25 ***

3,35 0,22 ***

3,27 0,20 ***

4,68

Experience Usage Intention 0,27 ***

3,60 0,27 ***

3,64 0,04

0,67 0,16 ***

3,77

Facilitating Conditions Usage Frequency 0,16 * 2,44 0,16

* 2,38 0,07

1,35 0,15

*** 4,31

Usage Intention Usage Frequency 0,62 ***

10,63 0,62 ***

9,87 0,55 ***

9,57 0,58 ***

15,47

GoF 0,76

0,71

0,71

0,73

*<.05, **<.01, ***<.001 Table 1. Model coefficient estimation results

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(S1/S3: t=1.73, p<.05; S2/S3: t=3.93, p<.01). These results support H1c. Social benefits

significantly increase attitude when customers are in the store (S2 and S3), whereas it does

not at S1. However, the difference between coefficients is only significant between S1 and S3

(S1/S2: t=.89, p>.1; S1/S3: t=1.87, p<.05). H1d is thus partially supported. Risks significantly

decrease attitude at S2 and S3. The parameters significantly differ between S1 and S2 (t=1.93,

p<.05) as well as between S1 and S3 (t=3.19, p<.01). As a result, H1e is confirmed.

Regarding the determinants of usage intention, the attitude significantly increases the usage

intention at every stages. As expected, the effect of social influence on usage intention is

significant when customers are in the store (S2 and S3). There are marginally significant

differences between the parameters of S1 and S2 (S1/S2: t=1.43, p<.1) and 3 (S1/S3: t=1.35,

p<.1). H2a is partially confirmed. Experience with the smartphone for shopping activities

positively impacts smartphone usage intention in S1 and S2 and there are significant

differences with S3 (S1/S3: t=2.44, p<.01; S2/S3: t=2.45, p<.01). Thus, it supports H2b.

Facilitating conditions only increase use at the first two stages, but differences between stages

are not significant (S1/S3: t=1.17, p>.1; S2/S3: t=.85, p>.1). H3 is partially supported.

5. Discussion and conclusion

First, reflecting emerging shopping behaviors, we consider smartphones to be a

complementary channel that facilitates in-store purchases. We detail the tasks involved and

identify smartphone functionalities supporting them and induced risks. Second we

demonstrate that perceived risks and benefits are key antecedents of customer attitude towards

smartphone usage for an in-store purchase, that attitude, social norms and experience

influence smartphone usage intentions and that usage intentions and facilitating conditions in

turn determine smartphone usage. Third, because each stage involves a different task calling

for relevant specific channel functions (Konus et al., 2008), we demonstrate the moderating

effect of the shopping stage. More precisely, in the pre-shopping stage (S1), when consumers

are searching for the right store, access to information and savings are the main benefits

driving intentions. Later, in the pre-purchase stage (S2), because users are interested in

finding the right product in the store while maintaining their privacy, information benefits and

risks are particularly important. Finally, during the purchase stage (S3), consumers adopt the

smartphone if they perceive it to be a convenient risk-free payment method and helpful to

minimize costs through mobile coupons. Shoppers use their smartphones to strengthen their

social identity when they are surrounded by other customers in the store (S2 and S3).

Experience and facilitating conditions are influential at S1 and S2 because mobile payment is

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still uncommon even among experienced users with access to all resources.

Our findings can help retailers determine the best mobile strategies. During S1, shoppers

search for information and promotional offers. Retailers could develop mobile applications or

a mobile website that helps them to be more easily located and to promote their offers, their

convenient hours and their services. Retailers could also provide shopping tips and mobile

coupons to be redeemed at the stores. For S2, what matters is to ensure access to product

information, project a positive image of oneself, but also avoid privacy concerns. Retailers

could provide quick answers through the smartphone to queries regarding products, while

protecting privacy. Finally, at S3, retailers should focus on convenience and access to

promotions, while ensuring financial security.

Our results also suggest a need for retailers to develop specific strategies for developing m-

commerce, even towards experienced users. With higher financial security of m-transactions

and new features adding value to mobile payment (e.g., free delivery, direct link with

customers’ accounts or loyalty programs), users might better understand the potential benefits

of paying through the smartphone.

As the goal was to focus on smartphone usage, we did not include other online

environments accessible through laptops and tablets. Further research could investigate

customers’ motivations to use all channels simultaneously or only one depending on

situational factors, such as the purchase level of urgency. Another limitation is the

generalization of our results, as our study is about a purchase for a compact camera and thus

about a single product category. Future studies could investigate other categories for which

the smartphone is often used as a complementary channel to an in-store purchase, such as

household appliances, groceries or fashions, and examine whether the results hold. Another

interesting research could be to investigate the effects of strategies encouraging m-commerce

with dedicated apps or mobile websites and higher perceived financial security. Finally, a

potentially fruitful research direction could be the investigation of the effects of smartphone

versus tablet usage on consumer loyalty to a particular retailer.

Key References

Balasubramanian S., Raghunathan R. and Mahajan, V. (2005), Consumers in a multichannel

environment: Product utility, process utility, and channel choice, Journal of Interactive

Marketing, 19, 2, 12-30.

Bauer R. (1967), Consumer behavior as risk taking, in D. Cox (Ed.), Risk taking and

information handling in consumer behavior, Cambridge, MA, Harvard University Press.

13

Bauer H. H., Barnes S. J., Reichardt T. and Neumann M. M. (2005), Driving consumer

acceptance of mobile marketing: A theoretical framework and empirical study, Journal of

Electronic Commerce Research, 6, 3, 181-192.

Bell D. R., Ho T. H. and Tang C. S. (1998), Determining where to shop: Fixed and variable

costs of shopping, Journal of Marketing Research, 35, 2, 352-369.

Bell D.R., Corsten D. and Knox, G. (2011), From point of purchase to path to purchase: How

preshopping factors drive unplanned buying, Journal of Marketing, 75, 1, 31-45.

Briesch R. A., Chintagunta P. K. and Fox, E. J. (2009), How does assortment affect grocery

store choice?, Journal of Marketing Research, 46, 2, 176-189.

Childers T. L., Carr C. L., Peck J. and Carson S. (2001), Hedonic and utilitarian motivations

for online retail shopping behavior, Journal of Retailing, 77, 4, 511-535.

Chin W. W. (1998), Commentary: Issues and opinion on structural equation modeling.

Chin W.W. and Newsted P.R. (1999), Structural equation modelling analysis with small

samples using Partial Least Square in R. Hoyle (Ed.), Statistical strategies for small

sample research, Thousand Oaks, Ca, Sage Publications, 307-341.

Eberl M. (2010), An application of PLS in multi-group analysis: The need for differentiate

corporate-level marketing in the mobile communications industry, in V. Esposito Vinzi,

W. W. Chin, J. Henseler and H. Wang (Eds.), Handbook of partial least squares:

Concepts, methods and applications in marketing and related fields, Berlin, Germany,

Springer-Verlag, 487-534.

Featherman M.S. and Pavlou P.A. (2003), Predicting e-services adoption: A perceived risk

facets perspective, International Journal of Human-Computer Studies, 59, 4, 451-474.

Frambach R., Roest H. and Krishnan T. (2007), The impact of consumer internet experience

on channel preference and usage intentions across the different stages of the buying

process, Journal of Interactive Marketing, 21, 2, 26-41.

Fishbein M. and Ajzen I. (1975), Belief, attitude, intention, and behavior: An introduction to

theory and research, Reading, MA, Addison-Wesley.

Fornell C. and Cha J. (1994), Partial least squares, in R. P. Bagozzi (Ed.), Advanced methods

of marketing research, Cambridge, MA, Blackwell, 52-78.

Fornell C. and Larcker D. F. (1981), Structural equation models with unobservable variables

and measurement error: Algebra and statistics, Journal of Marketing Research, 18, 3,

382-388.

Gensler S., Verhoef P. C. and Böhm M. (2012), Understanding consumers’ multichannel

choices across the different stages of the buying process, Marketing Letters, 23, 4, 987-

14

1003.

Goodhue D. L. and Thompson R. L. (1995), Task-technology fit and individual performance,

MIS Quarterly, 213-236.

Henseler J., Ringle C. and Sinkovics R. (2009), The use of partial least squares path modeling

in international marketing, Advances in International Marketing, 2, 277-320.

Hérault S. and Belvaux B. (2014), Privacy paradox et adoption de technologies intrusives : Le

cas de la géolocalisation mobile [Privacy paradox and the adoption of intrusive

technologies. The case of mobile location-based services], Décisions Marketing, 74,

April-June, 67-82.

Jarvenpaa S.L., Tractinsky N. and Vitale M. (2000), Consumer trust in an internet store,

Information Technology and Management, 1, 1-2, 45-71.

Khajehzadeh S., Oppewal H. and Tojibet, D. (2014), Consumer responses to mobile coupons:

The roles of shopping motivation and regulatory fit, Journal of Business Research, In

Press.

Kumar A. and Mukherjee A. (2013), Shop while you talk: Determinants of purchase

intentions through a mobile device, International Journal of Mobile Marketing, 8, 1, 23-

37.

Laukkanen T., Sinkkonen S., Kivijarvi M. and Laukkanen P. (2007), Innovation resistance

among mature consumers, International Journal of Marketing, 24, 7, 419-427.

Lee M. C. (2008), Factors influencing the adoption of internet banking: An integration of

TAM and TPB with perceived risk and perceived benefit, Electronic Commerce

Research and Applications, 8, 3, 130-141.

Lee L. and Ariely D. (2006), Shopping goals, goal concreteness, and conditional promotions,

Journal of Consumer Research, 33, 1, 60–70.

Levy M. and Weitz B. (2012), Retailing management (8th Edition), New York, McGraw-Hill

Higher Education.

Miller R. K. and Washington K. (2013), Part II: Shopping behaviors: Chapter 10: Mobile

shopping, in Consumer behavior, Loganville, GA, R. K. Miller & Associates, 65-70.

Mimouni-Chaabane A. and Volle P. (2010), Perceived benefits of loyalty programs: Scale

development and implications for relational strategies, Journal of Business Research, 63,

1, 32-37.

Morris M.G., Venkates, V. and Ackerman P.L. (2005), Gender and age differences in

employee decisions about new technology: An extension to the theory of planned

behavior, IEEE Transactions on Engineering Management, 52, 1, 69-84.

15

Mourali M., Laroche M. and Pons, F. (2005), Antecedents of consumer relative preference for

interpersonal information sources in pre‐purchase search, Journal of Consumer

Behaviour, 4, 5, 307-318.

Murray K. B. and Schlacter J. L. (1990), The impact of services versus goods on consumers’

assessment of perceived risk and variability, Journal of the Academy of Marketing

Science, 18, 1, 51-65.

Powers T., Advincula D., Austin M. S., Graiko S. and Snyder J. (2012), Digital and social

media in the purchase decision process: A special report from the advertising research

foundation, Journal of Advertising Research, 52, 4, 479-489

Ringle C. M, Wende S. and Will A. (2005), SmartPLS 2.0., www.smartpls.de, Hamburg.

Shankar V., Venkatesh A., Hofacker C. and Naik P. (2010), Mobile marketing in the retailing

environment: Current insights and future research avenues, Journal of Interactive

Marketing, 24, 2, 111-120.

Sheth J. N., Newman B. I. and Gross B. L. (1991), Why we buy what we buy: A theory of

consumption values, Journal of Business Research, 22, 2, 159-170.

Sweeney J. C. and Soutar G. N. (2001), Consumer perceived value: The development of a

multiple item scale, Journal of Retailing, 77, 2, 203-220.

Taylor S. and Todd P. (1995), Decomposition and crossover effects in the theory of planned

behavior: A study of consumer adoption intentions, International Journal of Research in

Marketing, 12, 2, 137-155.

Turban E., King D., Le, J. and Viehland D. (2004), Electronic commerce: A managerial

perspective, Englewood Cliffs, Prentice Hall.

Van der Heijden H. (2006), Mobile decision support for in-store purchase decisions, Decision

Support Systems, 42, 656-663.

Vanheems R. (2013), La distribution multi-canal : une redéfinition du rôle du vendeur [Multi-

channel retailing: towards a redefinition of the salesman’s mission], Décisions Marketing,

69, January-March, 43-59.

Varshney U., Vetter R. J. and Kalakota R. (2000), Mobile commerce: A new frontier,

Computer, 33, 10, 32-38.

Venkatesh V., Thong J. and Xu X. (2012), Consumer acceptance and use of information

technology: Extending the unified theory of acceptance and use of technology, MIS

Quarterly, 36, 1, 157-178.

Wells J. D., Campbell D. E., Valacich J. S. and Featherman M. (2010), The effect of

perceived novelty on the adoption of information technology innovations: A risk/reward

16

perspective, Decision Sciences, 41, 4, 813-843.

Yang B., Kim, Y. and Yoo C. (2013), The integrated mobile advertising model: The effects of

technology- and emotion-based evaluations, Journal of Business Research, 66, 1345-

1352.

Zhang J. and Mao E. (2008), Understanding the acceptance of mobile SMS advertising among

young Chinese consumers, Psychology & Marketing, 25, 8, 787-805.

Appendix 1. Survey scenarios and survey flows

S1: [Read Introduction] [Read Stage 1 Scenario] [Measurement of smartphone usage

intention at stage 1] [Measurement of all stage-related constructs (e.g., usage attitude,

benefits, risks, …] [Read Stage 2 Scenario] [Measurement of smartphone usage int. at

stage 2] [Read Stage 3 Scenario] [Measurement of smartphone usage int. at stage 3]

S2: [Read Introduction] [Read Stage 1 Scenario] [Measurement of smartphone usage

intention at stage 1] [Read Stage 2 Scenario] [Measurement of smartphone usage int. at

stage 2] [Measurement of all stage-related constructs (e.g., usage attitude, benefits, risks,

…] [Read Stage 3 Scenario] [Measurement of smartphone usage int. at stage 3]

S3: [Read Introduction] [Read Stage 1 Scenario] [Measurement of smartphone usage

intention at stage 1] [Read Stage 2 Scenario] [Measurement of smartphone usage int. at

stage 2] [Read Stage 3 Scenario] [Measurement of smartphone usage int. at stage 3]

[Measurement of all stage-related constructs (e.g., usage attitude, benefits, risks, …]

INTRODUCTION

This study focuses on your opinion regarding the use of smartphones for shopping. You will

find below a description of the various usages of your smartphone for shopping. Before

entering a store, the smartphone can provide information by connecting you to the Internet or

by using an application (from retailers, from brands, from comparators, etc.). Outside of the

store, information can include the location of a nearby store, opening hours, features and

prices of several products, promotional offers and availability of products in the store and

reviews from consumers or price comparisons. In the store, in addition to the features already

mentioned, scanning a QR code can provide detailed information on a product, access to a

video or instant rewards. Smartphones can also be used to photograph the product and seek

advice from others by sending the photo by MMS or by posting it on social networks. Some

stores also offer applications that facilitate your shopping by providing advice and

information on where to find your product. Finally, when buying the product, it is also

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possible in some stores to pay with your smartphone using applications such as Google

wallet. Smartphones can be used as an electronic credit card. This electronic portfolio allows

you to store your loyalty cards and receive promotional offers from retailers. Your list of

recent transactions can also be accessed at any time.

You are now asked to read the text below and then answer to a set of questions related to it.

Stage 1 – PRE-SHOPPING “outside the store”

Imagine that you want a new compact camera with a powerful zoom, a large screen and at an

attractive price. You're not at home. You do not know which store has the best offer or what

product or brand is suitable for your needs. You begin to gather information on retailers and

their offers in order to select the store to visit and evaluate products.

Stage 2 – PRE-PURCHASE “in store”

Imagine that you want a new compact camera with a powerful zoom, a large screen and at an

attractive price. You're not at home. You do not know which store has the best offer or what

product or brand is suitable for your needs. You begin to gather information on distributors

and their offers to select the store to visit and evaluate products. Now imagine that for this

camera, you now have gathered information on distributors and their offers to select the store

to visit. You have also collected information on products. You have chosen a store where

several options seem to meet your needs. You are now entering the store to select the product

that best suits you by comparing offers and looking for outside opinions.

Stage 3 – PURCHASE “in store”.

The purchase can be either done through the phone or at the store cashier. Imagine that you

want a new compact camera with a powerful zoom, a large screen and at an attractive price.

You're not at home. You do not know which store has the best offer or what product or brand

is suitable for your needs. You begin to gather information on distributors and their offers to

select the store to visit and evaluate products. Now imagine that for this camera, you now

have gathered information on distributors and their offers to select the store to visit. You have

also collected information on products. You have chosen a store where several options seem

to meet your needs. You are now entering the store to select the product that best suits you by

comparing offers and looking for outside opinions. Now imagine that for this camera you

chose a store and checked out the products to choose the one that best suited you. You

decided on a product and you are ready to proceed to purchase. The store where you are has

a system of direct payment by smartphone. Simply approach your smartphone terminal and

validate the payment on your smartphone to pay for your purchase.

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Appendix 2. Scale items, summary statistics, standard loadings, composite reliability and average variance extracted

Constructs and measured items Standard Loadings

Stage 1 (S1) Stage 2 (S2) Stage 3 (S3) Total sample

Attitude towards smartphone usage* (mean=3.88; SD=1.97) α ; CR ; AVE .97; .98; .91 .96; .97; .89 .96; .97; .90 .98; .98; .93

Using a smartphone would be a Bad/Good idea .94 .95 .97 .96

Using a smartphone would be a Foolish/Wise idea .92 .93 .95 .94

I have an Unfavorable/favorable opinion about using a smartphone .96 .96 .96 .96

I have a Negative/ positive opinion about using a smartphone .95 .95 .97 .96

Economic benefits* (mean=3.73; SD=1.80) α ; CR ; AVE .95; .97; .90 .96; .97; .93 .96; .97; .92 .92; .95; .97

Using a smartphone would allow me to do my shopping at a lower financial cost .96 .97 .93 .96

Using a smartphone would allow me to save money .98 .97 .96 .97

Using a smartphone would allow me to take advantage of promotional offers .94 .93 .90 .92

Convenience benefits* (mean=4.29; SD=1.85) α ; CR ; AVE .96; .98; .94 .97; .98; .94 .96; .98; .93 .97; .99; .94

Using a smartphone would allow me to save time .97 .97 .96 .97

Using a smartphone would make my shopping less time consuming .97 .97 .98 .97

Using a smartphone would be a convenient way to do shopping .97 .95 .96 .96

Information benefits* (mean=4.57; SD=1.72) α ; CR ; AVE .97; .98; .94 .96; .98; .93 .97; .98; .94 .97; .98; .94

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Using a smartphone would allow me to get information about stores and products .96 .97 .97 .97

Using a smartphone would allow me to get information about product price comparison .97 .97 .97 .97

Using a smartphone would allow me to get useful info. to make better shopping

decisions .96 .96 .97 .97

Social benefits* (mean=2.92; SD=1.80) α ; CR ; AVE .96; .97; .93 .98; .99; .96 .95; .97; .90 .95; .97; .92

Using a smartphone would help me make a good impression on other people .97 .94 .94 .95

Using a smartphone would help me feel acceptable .98 .95 .96 .97

Using a smartphone would improve the way I am perceived by others .98 .96 .97 .97

Risks* (mean=4.26; SD=1.67) α ; CR ; AVE .94; .95; .79 .94; .89; .63 .95; .96; .83 .95; .96; .83

Using a smartphone would cause me to lose control over my privacy .91 .69 .69 .93

Using a smartphone would lead to a loss of privacy because my personal information

would be used without my knowledge .95 .65 .65 .93

Using a smartphone would lead me to run the risk of internet hackers taking control of

my personal information .71 .96 .96 .84

Using a smartphone would lead to potential fraud of my checking account .91 .89 .89 .89

Using a smartphone would subject my checking account to financial risks .93 .74 .74 .94

Facilitating conditions* (mean=4.58; SD=1.65) α ; CR ; AVE .96; .95; .82 .96; .95; .83 .91; .94; .79 .93; .95; .82

I have the resources necessary to use a smartphone .94 .88 .92 .92

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I have the knowledge necessary to use a smartphone .93 .93 .91 .93

I have the ability to use a smartphone .95 .94 .92 .94

I would be able to use a smartphone without any internet network access issues .82 .80 .88 .84

Social influences* (mean=3.06; SD=1.69) α ; CR ; AVE .95; .97; .90 .95; .97; .90 .93; .96; .88 .93; .97; .92

People who are important to me think that I should use a smartphone .95 .93 .95 .95

People who influence my behavior think that I should use a smartphone .95 .94 .97 .95

People whose opinions that I value prefer that I use a smartphone .94 .95 .96 .95

Usage intention* (mean=3.61; SD=2.06) α ; CR ; AVE .97; .98; .94 .96; .98; .93 .96; .97; .92 .98; .98; .95

I intend to use a smartphone .97 .97 .98 .98

I plan to use a smartphone .95 .96 .97 .96

I will use a smartphone in the future .97 .95 .98 .97

Experience* (mean=3.73; SD=1.88) α ; CR ; AVE .96; .97; .86 .97; .97; .88 .95; .96; .83 .96; .97; .83

I have a great deal of experience with using smartphones when doing shopping .94 .93 .92 .94

I have used to or been exposed to using smartphones when doing shopping in the past .94 .90 .93 .92

I am familiar with the different possibilities of using smartph. when doing shopping .94 .90 .95 .93

I frequently inform myself on the possibilities of using smartph. when doing shopping .92 .90 .89 .91

I am very confident in using a smartphone when doing shopping .95 .92 .95 .94

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* Except the experience construct, all constructs were measured with respect to using a smartphone at the stage respondent were assigned to.